3 papers
cs.AI2026
OPTScientist: Multi-Agent Discovery of Typed Optimizer Programs for Transformer Pretraining
Zhongzheng Li, Tiancan Feng, Wenhao Li +5
Designing optimizers for modern deep learning remains a challenging scientific problem, requiring the joint consideration of optimization geometry, state dynamics, numerical stabil…
cs.LG2025
ExLLM: Experience-Enhanced LLM Optimization for Molecular Design and Beyond
Nian Ran, Yue Wang, Xiaoyuan Zhang +4
Molecular design involves an enormous and irregular search space, where traditional optimizers such as Bayesian optimization, genetic algorithms, and generative models struggle to…
cs.LG2025
MCCE: A Framework for Multi-LLM Collaborative Co-Evolution
Nian Ran, Zhongzheng Li, Yue Wang +5
Multi-objective discrete optimization problems, such as molecular design, pose significant challenges due to their vast and unstructured combinatorial spaces. Traditional evolution…